Validity of a One-Stop Automatic Algorithm for Counting Clusters and Shifts in the Semantic Fluency Task

We introduce PROXIS, a computational algorithm for the Semantic Fluency Task (SFT), which automatically counts clusters and shifts. We compared its output relative to human coders and to another cluster/shift counting algorithm (Forager), and its performance in predicting executive functions (EF), intelligence, processing speed, and semantic retrieval, also against human coders and to Forager. Correlations with EF subdomains and other cognitive factors closely resemble those of human coders, evidencing convergent validity. We also used Naïve Bayes and Decision Tree for age classification, with PROXIS outputs successfully discriminating age groups, evidence of the meaning and interpretability of those counts. Clusters and shifts were found to be more important than word counts. PROXIS’s consistency extended across semantic categories (animals, clothing, foods), suggesting its robustness and generalizability. Comparing PROXIS convergent validity with Forager’s, we found that they are on par. However, PROXIS ability to discriminate between participants’ age groups is substantially higher than Forager’s. We believe that PROXIS is applicable beyond the specifics of the SFT, and to many tasks in which people list items from semantic memory (e.g., tasks like free associates, top-of-mind, feature listing). Practical implications of the algorithm’s ease of implementation and relevance for studying the relation of the SFT to EFs and other research problems are discussed.